Date of Award

2013

Degree Type

Thesis

Degree Name

Departmental Honors

Department

Mathematics and Statistics

First Advisor

Richard Cutler

Abstract

A common multivariate statistical problem is the prediction of two or more response variables using two or more predictor variables. The simplest model for this situation is the multivariate linear regression model. The standard least squares estimation for this model involves regressing each response variable separately on all the predictor variables. Breiman and Friedman [1] show how to take advantage of correlations among the response variables to increase the predictive accuracy for each of the response variable with an algorithm they call Curds and Whey. In this report, I describe an implementation of the Curds and Whey algorithm in the R language and environment for statistical computing [6], apply the algorithm to some example data sets, and discuss extensions of the algorithm to linear classification methods.

Share

COinS